Data-Driven Study of the the COVID-19 Pandemic via Age-Structured Modelling and Prediction of the Health System Failure in Brazil amid Diverse Intervention Strategies

Askery Canabarro; Elayne Tenorio; Renato Martins; Lais Martins; Samurai Brito; Rafael Chaves

is the (which was not peer-reviewed) The copyright holder for this preprint However, taken alone it leads to ≈ 27 millions infected and 723 thousand dead individuals (see the fifth columns in Tables V and VI ). An important observation is that our predictions are likely to be a lower bound to the actual numbers, since the confirmed cases are potentially underestimated given the lack of widespread clinical testing in Brazil. With more accurate data, it is possible that the fitting in Fig 2 would produce an even higher value for R 0 . In addition, our model can be applied to other countries or regions with minimal adaptation, since it will require only updates in Tables I -III . Finally, although we have employed many data-driven assumptions, the results presented here may still underestimate the threat to the national health system due to the particular social problems in Brazil, such as: (i) high level of cardiopathologies, a reported relevant comorbidity; (ii) considerable number of people without proper water and wasting supplies; (iii) large number of people living in the same house in peripheral zones due to housing deficit, (iv) high density of obesity cases and some other immune suppressant diseases, just to cite a few. On the positive side, there can be a potential defense against SARS-Cov-2 for those BCG-vaccinated [30] , which belongs to the universal vaccination program in Brazil [31] . Furthermore, the public health system in Brazil has great capillarity, in principle being capable of identify potential cases in the very beginning of the symptoms. However, that also requires widespread clinical tests to identify infected individuals.